Jul 2026· IEEE International Symposium on High-Performance Parallel Distributed Computing· 0 citations· 13 references
Computer Science
TL;DR
A modular implementation in Qiskit that supports non-binary alphabets and incorporates several key enhancements, including a deterministic BBHT-inspired Grover search, domain expansion via ancillary qubits to stabilize amplitude amplification, and circuit-level optimizations that reduce overhead are developed.
Abstract
We present a full-scale implementation and experimental evaluation of a quantum algorithm for the Longest Common Substring (LCS) problem in the circuit model, bridging the gap between recent theoretical advances and practical realization. Building upon a previously proposed \(\tilde{O}(\sqrt {n})\)-depth quantum circuit, we develop a modular implementation in Qiskit that supports non-binary alphabets and incorporates several key enhancements, including a deterministic BBHT-inspired Grover search, domain expansion via ancillary qubits to stabilize amplitude amplification, and circuit-level optimizations that reduce overhead. Our approach is validated through an extensive experimental campaign over a binary alphabet augmented with two termination symbols and length 16 demonstrating an overall accuracy of 98.4%. The results show that errors are both rare and small, with a consistent conservative bias toward underestimation, and that the algorithm maintains high performance across a wide range of input configurations. We further analyze the behavior of the algorithm under realistic noise models, showing a progressive degradation of accuracy and identifying a structural asymmetry in the error patterns induced by the oracle. These findings provide concrete evidence that circuit-based quantum algorithms for string processing can achieve reliable behavior in ideal settings, while highlighting key challenges for their deployment on noisy quantum devices.
Simulating quantum error correction (QEC) circuits including non-Clifford gates at scale is important to accelerate progress toward fault-tolerant quantum computing. Here we demonstrate that matrix product state (MPS) techniques can handle many QEC circuits exactly and without restriction on gate types. Crucially, we find that MPS efficiency depends sensitively on implementation choices, and we introduce a series of targeted optimizations that reduce bond dimensions and simulation time by several orders of magnitude compared to naive approaches. We illustrate this with examples including: (a) a rotated surface code quantum memory up to distance 11, (b) logical Bell-state preparation up to distance 9, (c) a 15-to-1 magic-state distillation circuit including hundreds of QEC rounds that we optimize to be simulated with only 11 logical qubits (187 physical qubits) and a maximal bond dimension of 64 in under 40 seconds, and (d) a narrow, deep random circuit that scales linearly with the number of T gates. These results demonstrate the importance of circuit-level optimizations and position MPS as a valuable complement to near-Clifford simulators for QEC circuits.
A. Orioli, Chen Zhao, G. Masella et al.· 0 citations
Suppressing errors is the central challenge for useful large-scale quantum computing. While quantum error correction promises a viable solution to this challenge, existing codes typically suffer from trade-offs among encoding efficiency, error threshold, and hardware feasibility. Here, we introduce Cornucopia codes, a family of practical, hardware-efficient quantum low-density parity-check codes that achieve an ultra-high encoding rate exceeding $1/2$ while maintaining a pseudo-threshold exceeding $0.4\%$ under the standard circuit-level noise model. Inspired by recent affine-permutation-based code constructions and the long-range connectivity available in reconfigurable neutral-atom arrays, we adopt a structured code geometry in which the code layout, atom rearrangement, and syndrome-extraction schedule are co-designed. This structure enables nonlocal syndrome measurements through simple, parallel atom rearrangements. A complete syndrome extraction cycle measures all $X$- and $Z$-type checks in parallel with $12$ entangling layers, independent of the code size. The resulting threshold is comparable to those of the surface code and bivariate bicycle codes. In particular, a single code block $[[2844,1426,18]]$ encodes $1{,}426$ distance-$18$ logical qubits, achieving an extrapolated logical error rate of $2.6\times10^{-16}$ ($1.9\times10^{-31}$) per logical qubit per cycle, assuming the physical error rate of $0.1\%$ ($0.01\%$). By comparison, a bivariate bicycle code implementation would require more than $68{,}000$ physical qubits to encode the same number of logical qubits at a comparable logical error rate. These results bring demonstrations of ultra-low-overhead quantum error correction within the reach of near-term quantum processors.
We investigate and compare the performance of two quantum optimization approaches, the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, applied to the Minimum Vertex Cover (MVC) problem. The problem is encoded as an and Ising model, and experiments are conducted on IBM’s GenericBackendV2 noisy superconducting qubit simulator and the D-Wave Advantage2 quantum annealer. Performance is evaluated in terms of solution quality, measurement probability, and proportion of valid solutions. The results we obtained show that, within our experimental setting, quantum annealing consistently outperforms its classical counterpart on small instances, while QAOA, though currently limited by simulation constraints, shows promising behavior that improves with increasing circuit depth. As problem size grows, both approaches exhibit sensitivity to parameter choices such as the penalty term and graph density, underscoring the need for careful tuning. These findings suggest that while both paradigms hold potential for combinatorial optimization, further advances in hardware capabilities and parameter calibration will be necessary to achieve reliable performance on larger instances.
Simone Faro, G. Messina, Damiano Muzzicato et al.· IEEE International Symposium...· 0 citations
This work reveals and exploits this underexplored robustness property: how much non-Clifford and variational expressivity can be removed from the sampling circuit before SQD accuracy degrades, and answers through two complementary compression techniques: gradient-based operator pruning, which discards low-impact excitation operators, and Clifford rounding, which snaps remaining parameters to the nearest Clifford angle.
Kangyu Zheng, Yidong Zhou, Jinglei Cheng et al.· 0 citations
Sampling-based proposals are prominent candidates for demonstrating quantum computations beyond the reach of classical supercomputers. However, it has been difficult to combine their complexity-theoretic hardness with two capabilities needed for scalable quantum computing more generally: suppressing hardware errors, and verifying the quantum computation itself. Here we address both issues by introducing structured circuits, which, in addition to provable hardness guarantees, admit an encoding in a quantum code. This allows us to simultaneously reach high fidelities at high circuit depths, and to certify an experimental fidelity via the circuit structure and measurement of code syndromes. The resulting certificate is device dependent, but requires substantially weaker noise assumptions than existing fidelity proxy benchmarks. We demonstrate our proposal with a $64$-qubit, depth-$73$ Clifford circuit, doped with $314$ $T$ gates. We use a total of $76$ physical qubits to encode this computation in spacetime codes, effectively suppressing gate error rates by $10\times$ after syndrome post-selection, and yielding a state with a fidelity lower bound of $0.349$ with $95\%$ confidence. Our construction is a systematic method for promoting a stabilizer state to a magic state while keeping an error-detected fidelity certificate.
S. Martiel, Jay-U. Chung, A. Seif et al.· 4 citations
We present an efficient implementation of the Parity Architecture for neutral-atom quantum processors. We adapt Parity Twine Networks (PTNs) to different atom layouts, native entangling gates, and atom-shuttling capabilities. This provides a general framework for hardware-aware optimization of gate count, circuit depth, and atom transport for quantum circuits encoding arbitrary interaction graphs in a common basis. Specifically, we develop PTN constructions based on different native entangling-gate realizations, namely CZ, CZSWAP, and iSWAP, providing flexibility to accommodate different hardware capabilities on both static and mobile neutral-atom platforms. Using the quantum Fourier transform (QFT) as a representative example, we demonstrate substantial reductions in two-qubit gate count, atom transport, and circuit depth. These resource savings translate into an estimated circuit fidelity three orders of magnitude higher than competing compilation strategies for a 30-qubit QFT. We further extend the construction to the recently introduced optimistic QFT and discuss the broader applicability of PTNs to other quantum algorithms on neutral-atom platforms.
Javad Kazemi, Michael Fellner, Riccardo J. Valencia-Tortora et al.· 0 citations
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