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ARXIV:2604.06135 · QUANTUM MACHINE LEARNING · SUBMITTED 08 APR · 03:22 UTC · FRESHNESS UNKNOWN
ARXIV:2604.06135QUANTUM MACHINE LEARNINGSUBMITTED 08 APR · 03:22 UTCFRESHNESS UNKNOWNBasil Kyriacou · Viktoria Patapovich · Maniraman Periyasamy · Alexey Melnikov · arXiv
Shot-Based Quantum Encoding (SBQE) for quantum neural networks that improves data loading efficiency and performance on noisy hardware.
Opportunity summary
Pain Shot-Based Quantum Encoding (SBQE) for quantum neural networks that improves data loading efficiency and performance on noisy hardware.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
Shot-Based Quantum Encoding (SBQE) for quantum neural networks that improves data loading efficiency and performance on noisy hardware. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-space capacity or require circuit…
Efficient data loading remains a bottleneck for near-term quantum machine-learning. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-space capacity or require circuit depths that exceed the coherence budgets of noisy…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We show that SBQE is structurally equivalent to a multilayer perceptron whose weights are realised by quantum circuits, and we describe a hardware-compatible implementation…
Quantum Machine Learning moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
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Shot-Based Quantum Encoding (SBQE) for quantum neural networks that improves data loading efficiency and performance on noisy hardware.
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10.48550/arXiv.2604.06135Shot-Based Quantum Encoding (SBQE) for quantum neural networks that improves data loading efficiency and performance on noisy hardware.
Abstract
Efficient data loading remains a bottleneck for near-term quantum machine-learning. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-space capacity or require circuit depths that exceed the coherence budgets of noisy intermediate-scale quantum hardware. We introduce Shot-Based Quantum Encoding (SBQE), a data embedding strategy that distributes the hardware's native resource, shots, according to a data-dependent classical distribution over multiple initial quantum states. By treating the shot counts as a learnable degree of freedom, SBQE produces a mixed-state representation whose expectation values are linear in the classical probabilities and can therefore be composed with non-linear activation functions. We show that SBQE is structurally equivalent to a multilayer perceptron whose weights are realised by quantum circuits, and we describe a hardware-compatible implementation protocol. Benchmarks on Fashion MNIST and Semeion handwritten digits, with ten independent initialisations per model, show that SBQE achieves 89.1% +/- 0.9% test accuracy on Semeion (reducing error by 5.3% relative to amplitude encoding and matching a width-matched classical network) and 80.95% +/- 0.10% on Fashion MNIST (exceeding amplitude encoding by +2.0% and a linear multilayer perceptron by +1.3%), all without any data-encoding gates.
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PROBLEM
Shot-Based Quantum Encoding (SBQE) for quantum neural networks that improves data loading efficiency and performance on noisy hardware. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-space capacity or require circuit depths that e...
METHOD
Efficient data loading remains a bottleneck for near-term quantum machine-learning. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-space capacity or require circuit depths that exceed the coherence budgets of noisy intermediate-sc...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We show that SBQE is structurally equivalent to a multilayer perceptron whose weights are realised by quantum circuits, and we describe a hardware-compatible implementation protocol. Code availability is...
WHY NOW
Quantum Machine Learning moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Shot-Based Quantum Encoding (SBQE) for quantum neural networks that improves data loading efficiency and performance on noisy hardware. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-space capacity or require circuit depths that exceed the coherence budgets of noisy intermediate-scale quantum hardware.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Efficient data loading remains a bottleneck for near-term quantum machine-learning. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-space capacity or require circuit depths that exceed the coherence budgets of noisy intermediate-scale quantum hardware.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We show that SBQE is structurally equivalent to a multilayer perceptron whose weights are realised by quantum circuits, and we describe a hardware-compatible implementation protocol. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Quantum Machine Learning moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Shot-Based Quantum Encoding (SBQE) for quantum neural networks that improves data loading efficiency and performance on noisy hardware.
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Quantum Machine Learning
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