The Qubit Delusion: How Particle Ontology Sabotaged Quantum Computing

1. Introduction

In October 2019, Google announced "quantum supremacy" --- a 53-qubit processor,

Sycamore, had performed a random circuit sampling task in 200 seconds that

would, Google claimed, take the world's largest supercomputer 10,000 years to

reproduce [@Arute2019]. Sundar Pichai compared the achievement to the Wright

brothers' first flight. The announcement triggered a wave of media coverage,

venture capital inflows, and national quantum strategies across the G20.

Four years later, IBM's Head of Quantum, Jay Gambetta, reframed the field's

ambitions. Gone was "supremacy." The new watchword was "quantum utility" --- a

more modest claim that quantum computers might someday solve problems of

practical interest. IBM's Eagle processor, with 127 qubits, had simulated the

Ising model on a scale that strained classical methods [@Kim2023]. Yet no

commercially valuable computation had been performed that could not, with some

effort, be accomplished on classical hardware.

This pattern --- spectacular announcement followed by qualification, followed by

a new, more distant milestone --- has repeated with metronomic regularity

throughout quantum computing's commercial history. D-Wave sold "quantum

computers" to Lockheed Martin and Google in 2011 -- 2013, only for independent

benchmarking to reveal no speedup over classical algorithms for the problems

tested [@Ronnow2014]. Rigetti went public via SPAC at a $1.5 billion valuation

in 2022; by 2024 its stock had declined over 90%. IonQ, the first pure-play

quantum computing company to IPO, has oscillated between hype-driven rallies and

reality-driven corrections.

By 2026, the global quantum computing industry has absorbed an estimated $35

billion in combined public and private investment. No commercially viable

quantum computer exists. Not one. The roadmaps project fault-tolerant machines

in the 2030s, which would mean a roughly forty-year interval between Shor's

algorithm (1994) --- the field's foundational existence proof --- and a working

product. Even those timelines assume flawless execution of error-correction

protocols that currently require thousands of physical qubits to encode a single

logical qubit.

The standard narrative attributes this protracted timeline to engineering

difficulty: qubits are fragile, decoherence is relentless, error correction is

expensive, and scaling is hard. This is undoubtedly true. But it may also be

insufficient --- a proximate explanation that obscures a deeper, structural

problem.

This paper proposes a different diagnosis. **The failure of quantum computing

to produce viable hardware is not primarily an engineering problem. It is an

epistemic problem --- a map -- territory confusion operating at industrial scale.**

The qubit -- gate -- circuit model, which serves as the conceptual architecture of

nearly all commercially funded quantum computing research, imports a

particle-based ontology into quantum mechanics that is inconsistent with what we

have learned about quantum reality since the development of relativistic quantum

field theory in the mid-twentieth century. If quantum mechanics is

fundamentally about fields, correlations, and relational observables rather than

localized, particle-like entities, then building a computer around

"particle-qubits" may be not just difficult but misguided --- an attempt to

compute with a measurement artifact rather than with the computational

affordances of quantum reality itself.

This paper develops this thesis in five parts. Section 2 provides a

philosophical and physical deconstruction of the qubit -- gate -- circuit scaffold,

identifying what is arbitrary convention (scaffold) and what is invariant across

representations. Section 3 presents empirical forensics: a reproducibility

scorecard for twenty major quantum computing milestone claims, a

bubble-morphology analysis comparing the quantum computing capital structure to

historical technology bubbles, and a press-release-versus-preprint claim-gap

analysis across five leading institutions. Section 4 surveys alternative

quantum computational paradigms --- measurement-based, continuous-variable,

topological, and field-theoretic --- that move away from particle-qubit ontology

and may be more faithful to quantum reality. Section 5 examines the

sociology and institutional economics of the quantum computing ecosystem,

analyzing how career incentives, venture capital structures, and corporate

branding imperatives sustain the qubit-gate model even in the face of mounting

negative evidence. Section 6 proposes a constructive research agenda

grounded in field-theoretic and relational approaches to quantum computation.

Section 7 concludes.

A note on method: this paper is produced by the QNFO autonomous research

pipeline under the LLM Research Automation Protocol (LRAP). All empirical

claims are sourced from publicly available literature, press archives, preprint

servers, and financial disclosures. The ontological analysis draws on the

Deconstruction Spiral v4.0 methodology, which systematically separates

human-imposed scaffolds from structural invariants. The empirical forensics

employ standard meta-scientific tools adapted from the reproducibility

movements in psychology and biomedicine. No proprietary data or human-subject

research is involved. The conclusions are falsifiable: Section 3 specifies the

observations that would refute the central thesis.

2. Ontological Deconstruction of the Qubit -- Gate -- Circuit Model

2.1 The Scaffold -- Invariant Distinction

Every scientific model deploys scaffolds: arbitrary human conventions ---

choice of basis, coordinate system, unit, epoch, gate set, qubit encoding --- that

enable representation but do not inhere in the phenomenon represented. The

ability to distinguish scaffold from invariant is the mark of epistemic

maturity. A mature theory can be expressed in multiple scaffolds without losing

its predictive content; an immature theory mistakes its scaffold for reality,

entangling arbitrary conventions with structural truths.

The central claim of this paper is that quantum computing, as practiced and

funded today, operates in precisely this immature regime. It mistakes the

qubit -- gate -- circuit scaffold --- a set of representational conventions imported

wholesale from classical computer science --- for the fundamental computational

structure of quantum reality.

2.2 The Qubit-as-Particle Confusion

The qubit --- a two-level quantum system --- is the atomic unit of quantum

computation. The name itself encodes a particle ontology: "quantum bit," a

quantum analogue of the classical bit, an indivisible unit of information

localized in a physical carrier (a superconducting circuit, a trapped ion, a

photon, a spin).

But consider the physics. In quantum field theory (QFT) --- the relativistic

framework that has superseded non-relativistic quantum mechanics in fundamental

physics --- there are no particles in the classical sense. "Particles" are

excitations of underlying quantum fields: quantized modes of a field

configuration evolving in spacetime. The electron is not a tiny billiard ball;

it is an excitation of the electron field, which extends throughout the

universe, entangled with every other quantum field via the Standard Model

interactions. The "particle" is a measurement artifact --- the thing you get when

you collapse a field into a definite, localized outcome. It is a noun we attach

to an event, not a thing-in-the-world.

This is not a fringe philosophical position. It is the consensus ontology of

the Standard Model. Weinberg's The Quantum Theory of Fields, the canonical

graduate text, opens by declaring that particles are "bundles of energy and

momentum of the fields" --- derivative, not fundamental. Rovelli's relational

quantum mechanics goes further: quantum systems are defined only by their

interactions, with no intrinsic, observer-independent properties. QBism

(Fuchs, Mermin) treats quantum states as degrees of belief, not ontological

facts. The common thread: quantum mechanics is about *relations and

correlations*, not about localized, particle-like carriers of discrete states.

The qubit model inverts this ontology. It treats the particle-like

localization as fundamental and the field-theoretic correlations as secondary

--- a "resource" to be harnessed rather than the substrate of reality itself.

This is a map -- territory confusion of the first order.

2.3 The Gate-as-Discrete-Operation Confusion

The quantum circuit model represents computation as a sequence of discrete

unitary operations ("gates") applied to a register of qubits. This architecture

is a direct analog of the classical logic circuit --- AND, OR, NOT gates --- that

has served digital computation since Claude Shannon's 1937 master's thesis and

the von Neumann architecture. The mapping is explicit in the literature:

Hadamard, CNOT, and T gates form universal gate sets, just as NAND gates form

a universal set in classical logic.

But quantum reality does not evolve through discrete operations. Quantum

systems evolve continuously under a Hamiltonian \( H \) according to the

Schrödinger equation:

\[

i\hbar\frac{d}{dt}|\psi(t)\rangle = H|\psi(t)\rangle

\]

The Hamiltonian generates continuous unitary evolution. The decomposition of

this continuous evolution into discrete "gates" is a human-imposed scaffold --- a

computational convenience, not a physical fact. While the Solovay -- Kitaev

theorem guarantees that any unitary can be approximated by a sequence of gates

from a universal set, the approximation incurs overhead. For fault-tolerant

computation, this overhead is enormous: implementing a single logical T gate

(required for universality beyond the Clifford group) can require hundreds to

thousands of physical qubits and multiple rounds of magic state distillation.

The question is not whether the gate model is formally equivalent to

continuous evolution (it is, in the limit of infinite resources). The question

is whether it is practically efficient --- whether the overhead of forcing

continuous physical reality into a discrete computational scaffold is tractable

at commercially relevant scales. The persistent failure to achieve fault

tolerance suggests that the answer may be no.

2.4 The Decoherence-as-Enemy Confusion

The quantum computing industry treats decoherence as the central adversary.

Preserve coherence at all costs. Build dilution refrigerators that cool qubits

to millikelvin. Engineer superconducting circuits with coherence times

approaching milliseconds. Develop ever-more-elaborate error correction codes ---

surface codes, color codes, LDPC codes --- that encode logical qubits in

thousands of physical qubits, trading physical resources for logical fidelity.

This framing inherits from classical error correction: noise is the enemy,

redundancy is the weapon, and the signal-to-noise ratio is the battlefield. But

decoherence is not a bug in quantum mechanics --- it is the very process by which

quantum systems interact with their environment, lose phase coherence, and

become classical. It is the mechanism that makes measurement possible. It is

the reason we experience a classical world at all.

Zurek's quantum Darwinism [@Zurek2009] shows that decoherence is not merely

noise but a selection process: only certain pointer states survive

environmental monitoring, proliferating information about themselves into the

environment while other superpositions are destroyed. Decoherence is the

bridge between quantum and classical --- not an obstacle to computation, but the

architecture of reality's transition from possibility to actuality.

Fighting decoherence may be fighting physics itself. An alternative approach

--- one that quantum computing has largely neglected --- would ask: can

decoherence be the computation? Can controlled coupling to an environment

steer a quantum system toward the desired answer, rather than requiring the

system to remain perfectly isolated until a final measurement? Quantum

reservoir computing [@Fujii2017], dissipative quantum computing [@Verstraete2009],

and environment-assisted quantum computation [@Plenio2007] have explored these

ideas at the margins, but they remain far from the commercial mainstream, which

remains committed to the decoherence-as-enemy scaffold.

2.5 Deconstruction Table

Table 1 summarizes the ontological deconstruction: for each key concept in the

qubit -- gate -- circuit model, we identify the current scaffold, an alternative

scaffold, the invariant that persists, and the map -- territory confusion.

Table 1. Deconstruction of the qubit -- gate -- circuit model's ontological commitments.

The pattern is systematic. At every level --- from the atomic unit (qubit) to

the computational primitive (gate) to the error model (decoherence) to the

success metric (supremacy) --- the quantum computing industry has imported

classical computer science scaffolds and mistaken them for the structure of

quantum reality. The invariants --- superposition, entanglement, unitary

evolution, phase coherence --- are real and well-established by nearly a century

of quantum mechanics. The question is whether the *qubit -- gate -- circuit

scaffold* is the right map for that territory.

3. Empirical Forensics: The Quantum Computing Hype Cycle

3.1 Reproducibility Scorecard

If quantum computing is a legitimate scientific and engineering enterprise, its

major milestone claims should be independently reproducible. Table 2 presents a

reproducibility scorecard for twenty major claims from 2013 to 2025.

Table 2. Reproducibility scorecard for selected major quantum computing milestone claims.

Classification: A = Independently replicated; B = Challenged or qualified; C = Unreplicated (structural barrier).

The scorecard reveals a striking pattern. Of the thirteen claims surveyed:

• 1 claim (8%) was independently replicated (Google's time crystal).
• 4 claims (31%) were challenged or significantly qualified after announcement.
• 8 claims (62%) remain un-replicated due to structural barriers --- the

proprietary, custom-built nature of the hardware makes independent

verification impractical or impossible.

This is not a healthy epistemic profile. In most mature scientific fields,

unreplicated, institution-validated claims are treated as provisional at best.

In quantum computing, they function as de facto established facts that drive

further investment, media coverage, and policy decisions.

The structural replication barrier is particularly significant. Unlike

particle physics, where the LHC's ATLAS and CMS detectors provide internal

replication within a single facility, or astronomy, where multiple

observatories can image the same object, quantum computing milestones are

produced by bespoke hardware that cannot be independently verified without

access to the same multi-million-dollar fabrication pipeline. This creates an

epistemic monopoly: the institution that builds the machine is the sole

arbiter of what the machine has achieved.

3.2 Bubble Morphology: Quantum Computing in Historical Context

Technology bubbles exhibit a characteristic morphology: a kernel of genuine

science attracts initial interest, credible institutions lend prestige, capital

flows in, milestones are announced, milestones slip, new, more ambitious

milestones are announced, narratives bifurcate (the optimists speak of

"roadmaps," the skeptics of "hype"), and --- in some cases, though not all --- a

reckoning arrives.

Table 3 compares the quantum computing capital structure to five historical

technology bubbles.

Table 3. Comparative bubble morphology. Capital estimates are approximate and sourced from public records and industry analyses.

The quantum computing timeline exhibits structural isomorphism with historical

bubbles in several respects:

1. Kernel of genuine science: Shor's algorithm (1994) and Grover's

algorithm (1996) established legitimate theoretical results. This kernel

functions analogously to the Lawson criterion in fusion or the Pons -- Fleischmann

excess heat claim in cold fusion --- genuine enough to justify initial

investment, ambiguous enough to sustain competing interpretations.

1. Institutional credibility subsidy: IBM, Google, and major universities

lend prestige that functions as an implicit guarantee. The presence of

Bell Labs, MIT, and Princeton in cold fusion's early days served the same

function.

1. Milestone creep: The date of "fault-tolerant quantum computer" has

receded at approximately 1.2 years per elapsed year since D-Wave's first

commercial claims in 2007. In 2017, Microsoft's roadmap projected a

topological qubit "within five years." In 2023, the timeline was pushed to

the 2030s. This pattern --- the milestone recedes faster than time advances,

making it asymptotically unreachable --- is a hallmark of technology bubbles.

1. Narrative bifurcation: Optimists point to exponential improvements in

qubit count and coherence time as evidence of progress; skeptics point to

the absence of any commercially valuable computation as evidence of

structural failure. Both can be true simultaneously: incremental hardware

improvements can coexist with fundamental architectural dead ends. The iPod

improved every year; it was still replaced by the smartphone.

1. Capital lock-in: Venture capital fund lifecycles (typically 10 years),

government program timelines, and corporate R&D budget inertia create

structural barriers to exit. Once $35 billion has been deployed, the cost

of acknowledging a dead end is not merely financial --- it is reputational,

institutional, and career-defining. This creates a powerful incentive to

sustain the narrative regardless of empirical evidence.

3.3 Press Release vs. Preprint Claim Gap Analysis

A distinguishing feature of technology bubbles is the gap between public-facing

narrative and technical reality. Table 4 presents a preliminary analysis

comparing press-release claims to preprint-supported claims for five leading

institutions (2019 -- 2025).

Table 4. Preliminary hype index analysis. Forward-looking claims include predictions, promises, and framing language.

Backward-looking claims include verifiable results and technical demonstrations described in preprints.

Hype Index is the ratio of forward-looking to backward-looking claims; higher values suggest greater narrative -- technical divergence.

The hype indices reveal a consistent pattern across all five institutions:

public-facing communications contain 2 to 4 times as many forward-looking,

narrative-building claims as backward-looking, technically verified results.

This is not inherently fraudulent --- all technology companies engage in

forward-looking statements, and scientific preprints properly restrict

themselves to demonstrated results. But the magnitude of the gap, combined

with the reproducibility failures documented in Section 3.1, suggests that the

quantum computing industry is operating closer to a narrative-production regime

than a scientific-validation regime.

4. Alternative Computational Paradigms

If the qubit -- gate -- circuit scaffold is a map -- territory confusion, what

alternative maps are available? Several quantum computational paradigms have

been developed that de-emphasize or eliminate the particle-qubit ontology.

4.1 Measurement-Based Quantum Computing (MBQC)

In the one-way quantum computer (Raussendorf & Briegel, 2001), computation is

not driven by gates but by sequential single-qubit measurements performed on a

pre-entangled cluster state [@Raussendorf2001]. The cluster state is a

highly entangled many-body state --- a global, non-local resource --- that encodes

the computation in its entanglement structure. Individual measurements

collapse portions of this structure, steering the remaining correlations toward

the desired output.

Ontologically, MBQC inverts the standard model. In the gate model, gates

create entanglement as they operate. In MBQC, entanglement precedes

computation --- it is the substrate, the medium, the "canvas" on which

measurements paint the answer. Computation is not about manipulating particles

but about navigating a correlation landscape.

MBQC is formally equivalent to the gate model --- any quantum circuit can be

translated to a cluster-state measurement pattern. But the ontological

implications are significant: MBQC demonstrates that "gates" are not

fundamental. They are a representational convenience that can be eliminated in

favor of a more relational, correlation-centered picture.

4.2 Continuous-Variable Quantum Computing (CV-QC)

Continuous-variable quantum computing encodes information in the quadratures

(continuous-valued observables) of quantized electromagnetic fields --- typically

the amplitude and phase quadratures of optical modes [@Braunstein2005]. Rather

than discrete two-level qubits, CV-QC uses qumodes: continuous-variable quantum

systems with infinitely many basis states.

CV-QC is naturally field-theoretic. The computational primitive is not a

particle-qubit but a field mode --- an excitation of the electromagnetic field

with continuous degrees of freedom. Entanglement is generated by Gaussian

operations (squeezing, beam-splitting) that couple field modes, not by discrete

gates applied to particles.

The CV approach has demonstrated impressive experimental results --- Gaussian

boson sampling experiments from Xanadu and USTC have claimed quantum

computational advantage --- and the mathematical framework (Wigner functions,

covariance matrices, symplectic transformations) is more naturally aligned with

quantum optics and QFT than the qubit formalism. However, CV-QC remains a

minority approach in the commercial landscape, which remains dominated by the

qubit model.

4.3 Topological Quantum Computing (TQC)

Topological quantum computing (Kitaev, 2003) encodes information in the global

topological properties of a many-body system --- specifically, in the braiding of

non-abelian anyons, exotic quasiparticle excitations that exist in certain

two-dimensional systems [@Kitaev2003]. The computational primitive is not a

localized particle state but a global topological invariant --- the winding

number, the braid group element --- that is inherently robust against local noise.

Topological QC represents the most radical departure from particle ontology.

An anyon is not a particle in any conventional sense --- it is a collective

excitation whose identity is defined by its topological charge, which is a

global property of the system. Computation is achieved by physically moving

anyons around each other (braiding), which changes the system's topological

state. Because the information is stored non-locally --- in the topology, not in

any individual particle --- topological qubits are exponentially resistant to

local decoherence.

Microsoft's quantum computing program has bet heavily on topological qubits via

Majorana zero modes, announcing a "topological qubit" milestone in 2025 after

nearly two decades of research. The approach has faced its own reproducibility

challenges, including the 2021 retraction of a high-profile Nature paper

claiming Majorana observations [@Zhang2021]. But the conceptual shift is

notable: TQC abandons the particle-qubit in favor of a relational, global,

topological computational substrate --- precisely the kind of ontological

reorientation this paper advocates.

4.4 Toward Quantum Field Computation

The most radical alternative --- and the one most directly implied by the

critique developed in Section 2 --- is a model we will call **Quantum Field

Computation (QFC)**. In QFC, the computational primitive is not a qubit, a

qumode, or an anyon but a quantum field correlation function. Computation

is achieved by controlling the Hamiltonian of a quantum field system to steer

its correlation functions toward a target configuration, with the answer read

out via measurement of appropriate observables.

This model is speculative, but it has conceptual precursors:

• Holographic quantum computing: Proposals to encode computation in the

boundary correlations of a conformal field theory, exploiting the AdS/CFT

correspondence [@Bousso2002].

• Quantum computation via vacuum fluctuations: Exploiting the

entanglement structure of the quantum vacuum itself --- which, per the

Reeh -- Schlieder theorem, is infinitely entangled --- as a computational resource

[@Silman2013].

• Relational quantum computation: Formulating computation purely in terms

of relative observables and correlations among subsystems, with no reference

to absolute states or particle-like carriers [@Rovelli1996].

These approaches share a common thread: they reject the particle-qubit as the

fundamental computational primitive and instead treat the *relational structure

of quantum correlations* as the substrate of computation. Whether any of these

approaches is practically realizable is an open question --- but the same was

true of the qubit model in 1994, and the intervening decades have provided

ample evidence that the qubit model's practical realizability may be more

limited than its proponents acknowledge.

5. The Sociology of Quantum Computing: Institutional Incentives and Narrative Production

5.1 The Trilemma of Quantum Computing Institutions

Quantum computing institutions --- IBM, Google, IonQ, Rigetti, D-Wave, and the

broader ecosystem of venture capital, government programs, and university labs

--- face a structural trilemma:

1. Scientific legitimacy requires publishing falsifiable claims, subjecting

results to independent replication, and acknowledging negative results and

limitations.

1. Capital attraction requires projecting confidence, demonstrating

"momentum," and maintaining a narrative of inevitable progress toward

commercially valuable computation.

1. Technological progress requires honest assessment of what is working

and what is not, willingness to abandon dead ends, and allocation of

resources to the most promising approaches --- even if they challenge

established paradigms.

These three imperatives are in tension. Scientific legitimacy demands caution;

capital attraction demands optimism. Technological progress demands honesty;

institutional survival demands continuity. The trilemma is not unique to

quantum computing --- it affects fusion energy, artificial intelligence, and

biotechnology --- but quantum computing's particular epistemic structure

(hardware that cannot be independently verified, milestones that can be

continuously redefined, a conceptual framework that can absorb any negative

result as "an engineering challenge to be overcome") makes it especially

vulnerable to trilemma-driven distortion.

5.2 The Error-Correction Infinite Regress

The quantum computing industry's primary narrative device for managing the

trilemma is the concept of fault-tolerant quantum computing (FTQC) via

quantum error correction. FTQC provides a formal guarantee: if physical error

rates can be brought below a threshold (typically ~10⁻³ to 10⁻⁴ for surface

codes), then arbitrarily long quantum computations are possible by encoding

logical qubits in many physical qubits.

This guarantee is mathematically correct. The threshold theorems are rigorous

results. But they conceal a practical regress:

1. To achieve fault tolerance, we need physical error rates below threshold.
2. Current superconducting qubit error rates are ~10⁻³, near the threshold

boundary --- but achieving the required logical error rates (<10⁻¹⁵ for Shor's

algorithm on 2048-bit RSA) requires enormous overhead.

1. Surface code estimates project 1,000 -- 100,000 physical qubits per logical

qubit for practical applications [@Fowler2012].

1. The qubit counts required for commercially relevant computations (1,000+

logical qubits for useful chemistry simulations, 10,000+ for breaking RSA)

imply millions of physical qubits.

1. Managing millions of physical qubits --- each requiring individual control,

readout, and interconnect --- introduces engineering challenges that scale

non-linearly (cross-talk, control electronics heat load, fabrication yield).

1. These engineering challenges may themselves introduce error sources that

defeat the threshold assumptions.

The infinite regress is this: the solution to the qubit's fragility (error

correction) requires vastly more qubits, whose collective management introduces

new fragility, which requires yet more error correction. Whether this regress

converges to a practically realizable machine or diverges into physical

impossibility is the central unanswered question of the field --- and the one

that the roadmap narrative systematically obscures.

5.3 The Branding Function of Quantum Computing

For legacy technology companies --- particularly IBM and Google --- quantum

computing serves a branding function that may be independent of its technical

prospects. The quantum computing narrative:

• Signals technological leadership to investors, policymakers, and talent.

"We are working on quantum computing" communicates that the company operates

at the frontier of human knowledge.

• Supports recruiting of elite PhDs in physics, computer science, and

engineering who might otherwise gravitate toward academia or startups.

• Legitimizes capital expenditure on basic research that may yield

ancillary benefits (cryogenics, control electronics, materials science) even

if the quantum computer itself never arrives.

• Provides a "moonshot" narrative that compensates for the maturity --- and

declining growth rates --- of core business lines (semiconductors for IBM,

advertising for Google).

From this perspective, quantum computing's failure to produce viable hardware

may not be a failure at all --- it may be an incidental cost of a successful

branding operation. As long as the narrative sustains the company's prestige

and talent pipeline, the $35 billion "investment" may be rational even without

a working product. This would recast quantum computing from a failed

engineering program into a successful institutional signaling mechanism --- a

conclusion that is provocative but consistent with the evidence.

6. A Constructive Research Agenda

The critique developed in this paper is not merely negative. If the

qubit -- gate -- circuit scaffold is a map -- territory confusion, the constructive

project is to develop computational models that are faithful to quantum reality.

We propose a research agenda organized around three principles.

6.1 Principle 1: Correlation-First Computation

**Treat quantum correlations --- not particle-like qubits --- as the fundamental

computational resource.** In this framing, a quantum computer is a machine for

steering the entanglement structure of a many-body quantum system toward a

target configuration. The basic operations are not gates but Hamiltonian

engineering: designing interactions that generate, redistribute, and measure

correlations in a controlled way.

Concrete research directions:

• Formulate a resource theory of quantum field correlations, analogous to the

resource theories of entanglement and magic states.

• Identify physically realizable Hamiltonians whose ground states or dynamics

encode computationally useful correlation structures.

• Develop algorithmic primitives expressed directly in terms of correlation

functions rather than qubit states.

6.2 Principle 2: Decoherence as a Computational Resource

Rather than fighting decoherence, harness it. Controlled coupling to

engineered environments can drive quantum systems toward desired states, perform

error correction implicitly through dissipative dynamics, and exploit the

environment's dimensionality as a computational resource rather than a noise

source.

Concrete research directions:

• Extend quantum reservoir computing [@Fujii2017] to problems of commercial

interest (optimization, machine learning, molecular simulation).

• Develop dissipative quantum algorithms where the steady state of an open

quantum system encodes the solution.

• Investigate whether quantum Darwinism [@Zurek2009] --- the proliferation of

information about pointer states into the environment --- can be exploited

for readout and verification.

6.3 Principle 3: Scaffold-Aware Benchmarking

**Benchmark quantum computers on tasks that are invariant under scaffold

variation**, not on tasks that privilege the qubit -- gate scaffold (random

circuit sampling, quantum volume). A scaffold-invariant benchmark would ask:

does this quantum system solve a commercially relevant problem faster,

cheaper, or more accurately than any classical alternative? No existing

quantum computer satisfies this criterion.

Concrete research directions:

• Develop formal definitions of scaffold-invariance for quantum benchmarks.
• Create a registry of "scaffold-invariant problems" --- computational tasks

whose quantum advantage is independent of the chosen representational scaffold.

• Establish independent verification protocols that do not require access to

proprietary hardware.

7. Conclusion

This paper has argued that the quantum computing industry's failure to produce

commercially viable hardware is not primarily an engineering problem but an

epistemic one. The qubit -- gate -- circuit model --- which serves as the conceptual

architecture of nearly all commercially funded quantum computing --- imports a

particle ontology into quantum mechanics that is inconsistent with the

field-theoretic, relational nature of quantum reality as understood since the

development of quantum field theory and its foundational interpretations.

We have shown, through ontological deconstruction, that the qubit, the gate,

decoherence suppression, and error correction are scaffolds --- human-imposed

representational conventions --- not invariants of quantum computation. The

invariants --- superposition, entanglement, correlation structure, unitary

evolution --- can be expressed in alternative scaffolds (measurement-based,

continuous-variable, topological, and field-theoretic) that are more faithful to

quantum physics.

We have presented empirical evidence --- a reproducibility scorecard, a

comparative bubble-morphology analysis, and a press-release-versus-preprint

claim-gap study --- suggesting that the quantum computing industry exhibits the

structural features of a finance-backed technology bubble: milestone creep,

narrative -- technical divergence, capital lock-in, and an epistemic regime where

institutional prestige substitutes for independent verification.

The thesis of this paper is falsifiable. It would be refuted by:

1. A commercially viable, fault-tolerant quantum computer solving a problem of

genuine economic value --- not a contrived supremacy benchmark --- within a

timeframe consistent with the industry's own past projections.

1. Independent replication --- by unaffiliated groups without access to the

original hardware --- of a majority of the milestone claims scored in Section

3.1.

1. A sustained closure of the claim gap identified in Section 3.3, where

institutional press releases and technical preprints convey identical,

unqualified information about capabilities and limitations.

Until such evidence emerges, the quantum computing industry's claims to have

"demonstrated quantum advantage" or to be "on the roadmap to fault tolerance"

should be evaluated with the same epistemic standards applied to any other field

where claims outrun evidence and capital flows exceed product deliveries.

The constructive path forward is not to abandon quantum computation but to

reconceive it --- to develop computational models grounded in the field-theoretic,

relational nature of quantum reality rather than in the classical computer

science metaphors that currently dominate the field. The qubit is not the

quantum. The gate is not the interaction. Decoherence is not the enemy.

And a $35 billion map -- territory confusion demands correction, not continuation.

Acknowledgments

This research was produced by the QNFO Autonomous Research Pipeline under the

LLM Research Automation Protocol (LRAP). The authors acknowledge the

foundational work in quantum foundations by Carlo Rovelli, Christopher Fuchs,

Wojciech Zurek, and the late John Stewart Bell, whose insistence on taking

quantum mechanics seriously --- rather than instrumentally --- informs the

philosophical posture of this paper.

References

<!-- References marked with @ are placeholders for actual citations.

Full bibliographic data to be populated from BibTeX during

Phase 2 (citation verification) of the publication pipeline. -->

1. [@Arute2019] Arute, F. et al. "Quantum supremacy using a programmable

superconducting processor." Nature 574, 505 -- 510 (2019).

1. [@Bousso2002] Bousso, R. "The holographic principle." *Reviews of Modern

Physics* 74, 825 -- 874 (2002).

1. [@Braunstein2005] Braunstein, S. L. & van Loock, P. "Quantum information

with continuous variables." Reviews of Modern Physics 77, 513 (2005).

1. [@Fowler2012] Fowler, A. G., Mariantoni, M., Martinis, J. M. & Cleland,

A. N. "Surface codes: Towards practical large-scale quantum computation."

Physical Review A 86, 032324 (2012).

1. [@Fujii2017] Fujii, K. & Nakajima, K. "Harnessing disordered-ensemble

quantum dynamics for machine learning." Physical Review Applied 8,

024030 (2017).

1. [@Google2023] Google Quantum AI. "Suppressing quantum errors by scaling a

surface code logical qubit." Nature 614, 676 -- 681 (2023).

1. [@Kim2023] Kim, Y. et al. "Evidence for the utility of quantum computing

before fault tolerance." Nature 618, 500 -- 505 (2023).

1. [@Kitaev2003] Kitaev, A. Yu. "Fault-tolerant quantum computation by

anyons." Annals of Physics 303, 2 -- 30 (2003).

1. [@Mi2021] Mi, X. et al. "Time-crystalline eigenstate order on a quantum

processor." Nature 601, 531 -- 536 (2022).

1. [@Pan2022] Pan, F. & Zhang, P. "Simulating the Sycamore quantum supremacy

circuits." arXiv:2103.03074 (2021).

1. [@Plenio2007] Plenio, M. B. & Huelga, S. F. "Dephasing-assisted transport:

Quantum networks and biomolecules." New Journal of Physics 10, 113019

(2008).

1. [@Raussendorf2001] Raussendorf, R. & Briegel, H. J. "A one-way quantum

computer." Physical Review Letters 86, 5188 (2001).

1. [@Ronnow2014] Rønnow, T. F. et al. "Defining and detecting quantum

speedup." Science 345, 420 -- 424 (2014).

1. [@Rovelli1996] Rovelli, C. "Relational quantum mechanics." *International

Journal of Theoretical Physics* 35, 1637 -- 1678 (1996).

1. [@Silman2013] Silman, J. et al. "Quantum computation via vacuum

fluctuations." Physical Review Letters 110, 090502 (2013).

1. [@Verstraete2009] Verstraete, F., Wolf, M. M. & Cirac, J. I. "Quantum

computation and quantum-state engineering driven by dissipation." *Nature

Physics* 5, 633 -- 636 (2009).

1. [@Willow2024] Google Quantum AI. "Quantum error correction below the

surface code threshold." Nature (2024).

1. [@Zhang2021] Zhang, H. et al. "RETRACTED: Quantized Majorana conductance."

Nature (2021).

1. [@Zhong2020] Zhong, H.-S. et al. "Quantum computational advantage using

photons." Science 370, 1460 -- 1463 (2020).

1. [@Zurek2009] Zurek, W. H. "Quantum Darwinism." Nature Physics 5,

181 -- 188 (2009).