Companion paper to "The Qubit Delusion" (v1.1, 10.5281/zenodo.21254143). If the qubit-gate-circuit model is an epistemic failure — a projection of particle ontology onto relational, field-theoretic reality — what comes next? We strip quantum mechanics to its minimal ontological commitments, survey alternative paradigms (MBQC, CV-QC, TQC, field-theoretic, analog) for ontological fidelity, assess commercial manufacturability of each physical platform, and ask whether "quantum computing" is even the right frame. The deeper lesson — that computation IS a physical process and the substrate IS the algorithm — points toward thermodynamic, neuromorphic, and optical architectures where physics does the computing directly.
Companion paper to "The Qubit Delusion" (v1.1, 10.5281/zenodo.21254143). If the qubit-gate-circuit model is an epistemic failure — a projection of particle ontology onto relational, field-theoretic reality — what comes next? We strip quantum mechanics to its minimal ontological commitments, survey alternative paradigms (MBQC, CV-QC, TQC, field-theoretic, analog) for ontological fidelity, assess commercial manufacturability of each physical platform, and ask whether "quantum computing" is even the right frame. The deeper lesson — that computation IS a physical process and the substrate IS the algorithm — points toward thermodynamic, neuromorphic, and optical architectures where physics does the computing directly.
The maturation of quantum computing from theoretical physics to engineering reality presents a complex landscape for stakeholders navigating the transition from Noisy Intermediate-Scale Quantum (NISQ) devices toward Fault-Tolerant Quantum Computation (FTQC). This report provides a comprehensive, technically rigorous analysis of the current quantum ecosystem, examining the hardware mosaic of leading qubit modalities—superconducting circuits, trapped ions, neutral atoms, and silicon spin qubits—and evaluating them against operational metrics, including coherence times, gate fidelities, and cryogenic infrastructure requirements. We dissect the full software stack, differentiating the near-term utility of error mitigation techniques such as Zero-Noise Extrapolation (ZNE) from the long-term necessity of Quantum Error Correction (QEC) and the milestone of logical qubit break-even. The analysis benchmarks hybrid algorithms (VQE, QAOA) and circuit knitting strategies against current cloud-accessible hardware performance, while also addressing the imminent non-computational impact of PostQuantum Cryptography (PQC) migration. Experimental data synthesised from leading vendor roadmaps and pre-print literature indicate that superconducting platforms currently lead in gate speed (nanosecond regime) but face crosstalk scaling challenges beyond 1,000 qubits. In contrast, trapped-ion and neutral-atom architectures demonstrate superior connectivity and coherence. The report concludes with a grounded, evidence-based five-year projection, identifying high-value vertical use cases in pharmaceuticals and finance, and providing a strategic framework for enterprises to separate quantum hype from tangible commercial opportunity. Future research directions include the realisation of low-overhead Quantum LDPC codes and the development of modular photonic interconnects to scale beyond single-chip architectures.
Phase III of the Qubit Delusion series. Examines fundamental physical limits on computation: Landauer bound, Margolus-Levitin theorem, Bremermann limit, Bekenstein bound. Shows that quantum error-correction overhead (1e2-1e3 physical operations per logical operation) multiplies thermodynamic cost beyond what any polynomial algorithmic speedup can overcome. Proposes a falsifiable criterion: joules per solution on commercially relevant problems. Assesses all major quantum platforms against this criterion. Identifies reversible computing as the only paradigm that can approach the true physical limits. Full text and updates: https://papers.qnfo.org/papers/paper-physics-of-computation/
This work presents a comprehensive and rigorous examination of quantum computing, spanning its mathematical foundations, physical principles, algorithmic developments, hardware architectures, and philosophical implications. Beginning with linear algebra over Hilbert spaces and the postulates of quantum mechanics, the manuscript develops the formal structure of quantum information theory, including qubits, entanglement, quantum channels, and noise models. The work provides in-depth treatments of the quantum circuit model, alternative computation paradigms such as measurement-based, adiabatic, and topological quantum computation, and the theoretical framework of quantum error correction, including stabilizer and surface codes. Foundational and modern quantum algorithms are analysed in detail, including Shor’s factoring algorithm, Grover’s search, variational quantum eigensolvers, QAOA, and emerging quantum machine learning approaches. Beyond algorithms and implementations, the manuscript addresses scalability challenges, fault tolerance, computational complexity limits, and the implications of quantum computing for cryptography and the extended Church–Turing thesis. The concluding sections explore philosophical questions concerning interpretation, measurement, and the relationship between computation and physical reality. This work is intended as both a reference text and a research-level synthesis for advanced students, researchers, and practitioners in quantum computing, theoretical computer science, and quantum physics.
Since the 1980s, quantum mechanics has been imagined as the basis for a new kind of computer. In 1994, Peter Shor proved that a sufficiently large quantum computer would break public‑key cryptography in polynomial time. In the four decades since, an industry has formed around building such machines: superconducting transmons at IBM and Google, trapped ions at IonQ and Quantinuum, photonic qubits at PsiQuantum and Xanadu, topological qubits at Microsoft, neutral atoms at QuEra and Atom Computing. The largest current devices have on the order of one thousand to ten thousand physical qubits, with substantial effort directed at error correction, isolation from environmental noise, and gate‑fidelity improvements. The question that hangs over the entire program is: how far can it go? Is there a structural ceiling — a fundamental, framework‑independent limit on the number of qubits that can be held in coherent superposition, or on the depth of circuits that can be executed before decoherence overwhelms the computation? Or is the limit purely engineering? Standard physics treats decoherence as environmental coupling, with quantum error correction sufficient (in principle) to tolerate finite decoherence rates below fault‑tolerance thresholds. This is a coherent picture but leaves a structural question unanswered: is the substrate of the universe indifferent to whether a system is in a coherent superposition, or does the substrate place its own constraints on coherent superposition — constraints that engineering cannot work around because they are not engineering problems? This walkthrough presents the Event Density framework's answer. The substrate has finite local participation density, finite cross‑chain bandwidth, finite‑width V1 and V5 kernels, and primitive commitment‑irreversibility. These primitives are not silent on quantum computation; they jointly impose substrate‑level conditions on any system performing coherent quantum computation. The conditions are codified by Theorem UR‑1 (Unresolved‑Regime Characterization): bounded multiplicity, sustained cross‑chain bandwidth, and commitment‑injection below the individuation threshold. The conditions are jointly necessary and sufficient. Three substrate failure modes drive the three conditions to failure: premature individuation from environmental ED‑injection (F1), cross‑endpoint decoupling from Γ_cross collapse (F2), and commitment‑pressure cascade from accumulated P11 events (F3). Three architectural classes — Class A engineered‑low‑multiplicity, Class B global‑geometric‑rigidity, Class C high‑multiplicity‑redundancy — are exhaustive substrate‑permitted strategies. Meta‑architectures (error correction, dynamical decoupling, reservoir engineering, hybrids) are compositions over the three classes, not separate classes. The classes carry three sharp predictions. A Class A wall at the matter‑wave quantum‑classical boundary of 140–250 kDa molecular mass — currently 5–10 times beyond the largest demonstrated coherent superposition, with active experimental programs targeting that range. A Class B exponential coherence advantage in the topological‑protection regime, the substrate‑level reason topologically‑protected qubits (Majorana, Fibonacci anyons) outperform Class A qubits at sufficiently low temperatures. A Class C correlation‑budget plateau as the substrate‑level constraint on how far redundancy‑based architectures (large‑scale error correction, neutral‑atom arrays) can push qubit count before cross‑bandwidth saturates. The structural payoff: quantum computation has a substrate‑level architectural classification with falsifiable predictions, and the same substrate Γ_cross structure that produces black‑hole horizons and bipartite entanglement monogamy also produces the architectural classification of quantum‑computing platforms. The cross‑domain unification — quantum computing and black‑hole physics governed by the same substrate mechanism at scales separated by fifty orders of magnitude — is the framework's strongest piece of cross‑platform mechanism identity.
This thesis considers various paradigms of quantum computation in an attempt to understand the nature of the underlying physics. A standard approach is to consider unitary computation on pure input states, such that the outcome of the computation is determined by single computational basis measurement on the output state. It has been shown that there exists equivalent models of computation, such as measurement based quantum computing (MBQC), which provide insight into the role of entanglement and measurement. Furthermore, constraining or relaxing available resources can directly impacts the power of the computation, allowing one to gauge their role in the process. Here, we first extend known constructions such as Matrix Product States, MBQC and the oneclean qubit model to a mixed state formalism, in an attempt to develop computational models where noise acting on the physical resources, as might be experienced in laboratory settings, may be mapped to logical noise on the computation. Next, we introduce Measurement-Based Classical Computing, an essentially classical model of computation, wherein the complexity hard wired into probability distributions generated via quantum means yields surprising non classical results. Finally, we consider postselection the ability to discard displeasing measurement outcomes and argue that it may be used in a tame way, which does not provide a dramatic increase in computational power. From here, we develop a new Hamiltonian, based on a circuit to Hamiltonian construction, presenting evidence of QMA-hardness.