Daily Papers

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Google Willow chip demonstrates that increasing the size of a surface code reduces the logical error rate — operating below the error correction threshold for the first time with a superconducting device. A critical milestone toward fault-tolerant quantum computing.

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Google Willow processor demonstrates two surface code memories operating below the error correction threshold. Increasing the code distance from 5 to 7 reduces the logical error rate by a factor of 2.14. The logical qubit lifetime exceeds the best physical qubit by 2.4x.

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Demonstrates fault-tolerant quantum memory using low-density parity-check (LDPC) codes achieving an error threshold of 0.7% on par with surface codes but with dramatically lower qubit overhead. LDPC codes can encode more logical qubits per physical qubit than surface codes.

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Proves that specific circuit architectures with finite local depth and long-range entanglement can avoid barren plateaus even at scale. Provides a constructive route to trainable deep quantum circuits.

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Proves a direct mathematical connection between circuit expressibility and barren plateaus: circuits that can approximate 2-designs (highly expressive) necessarily have exponentially vanishing gradients. The trainability-expressibility trade-off is fundamental and inescapable.

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A comprehensive survey of variational quantum algorithms — covering design, training, applications to chemistry, optimization, and ML, alongside the key challenges: barren plateaus, noise, and the difficulty of demonstrating quantum advantage.

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Provides a rigorous proof-of-principle quantum advantage in supervised ML for a specific problem based on discrete logarithm — a problem believed to be hard classically but easy quantumly. The dataset was artificially constructed to demonstrate the separation.

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Quantum ML models do not automatically outperform classical ones. By carefully constructing classical kernels that mimic quantum feature maps, classical ML can match or exceed quantum models on most practical datasets. The key insight is that quantum advantage in ML requires data with genuine quantum structure.

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A comprehensive resource estimation for fault-tolerant quantum simulation of industrially relevant chemistry problems. Shows that simulating molecules like FeMoco for nitrogen fixation requires millions of T gates and thousands of logical qubits.

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A detailed resource estimation for running Shor's algorithm at practical scale. Breaking RSA-2048 requires approximately 20 million physical qubits running for 8 hours with surface code error correction at realistic error rates.

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Proves that all supervised quantum ML models are equivalent to kernel methods, where the kernel is defined by the inner product of quantum feature states. This unifies QML with classical kernel theory and provides rigorous tools for analyzing quantum models.

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Barren plateaus depend critically on whether the cost function is global or local. Local cost functions on shallow circuits can avoid barren plateaus even at scale, providing a partial path forward for trainable VQAs.

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Shows that a single qubit can act as a universal classifier by repeatedly re-uploading classical data at multiple circuit layers, interleaved with trainable rotations. Demonstrates that quantum advantage in ML does not require many qubits — it requires the right structure.

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A systematic review of parametrized quantum circuits as machine learning models, covering training strategies, expressibility, gradient computation methods including the parameter shift rule, and practical implementation considerations.

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Google 53-qubit Sycamore processor completed a specific random circuit sampling task in 200 seconds, estimated to take 10,000 years on the best classical supercomputer at the time. A landmark but contested result.

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Introduces quantitative measures for comparing parametrized quantum circuit designs — expressibility (how much of Hilbert space the ansatz can reach) and entangling capability (how much entanglement it generates on average).

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Shows that a quantum algorithm for recommendation systems claimed to give exponential speedup can be matched by a classical algorithm using quantum-inspired sampling techniques. This dequantization result triggered a re-evaluation of many proposed quantum ML speedups.

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Shows that for certain combinatorial optimization problems, classical algorithms match the performance of constant-depth QAOA. Challenges the assumption that shallow quantum circuits provide advantage over classical algorithms for optimization.

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For random parametrized quantum circuits, the gradient of the cost function vanishes exponentially in the number of qubits. This makes training quantum neural networks on random initializations effectively impossible at scale.

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NISQ devices with 50-100 noisy qubits may achieve tasks beyond classical simulation but fault-tolerant quantum computing remains a long-term goal. The term NISQ was coined here to frame realistic near-term expectations.