Qubits 2026

Yi Yang, Bin Wang, Jican Lin, Yang Gao, Xin Li, Jiu-Peng Chen, L. Hou, Yelei Wang, Yong Wan, Xiu-Ping Xie, Mingxuan Zheng, Qiang Zhang, Jian-Wei Pan

Quantum frequency conversion (QFC) is essential for bridging the spectral gap between stationary qubits and low-loss optical communication channels. In this work, we demonstrate a short-wavelength-pumping QFC with the first-order quasi-phase matching period of 3.07 um on thin-film lithium niobate, converting ultraviolet photons to the telecom C-band. By constructing a theoretical model that correlates the normalized conversion efficiency with domain defects in the short-period phase-matched waveguide, we found the critical tolerance of domain defects along the waveguide should be $\le 2$ (excluding the ends). Based on this, we achieved a theoretical limit normalized conversion efficiency of 839%/(W*cm^2) for the fundamental guided mode through fabrication optimization. Furthermore, we propose a robust noise suppression strategy for short-wavelength pumping by utilizing the counter-tuning behaviors of difference-frequency generation and spontaneous parametric down-conversion. By combining these advances with ultra-narrowband filtering, we achieve a record-high external efficiency of 28.8% and an ultra-low noise of 35 counts per second. This high-performance QFC connecting ultraviolet and telecom bands satisfies the stringent requirements for long-lived remote ion-ion entanglement in scalable quantum networks [W.-Z. Liu et al., Nature (2026)].

Ameya S. Bhave, Navnil Choudhury, Andrew Nemec, Kanad Basu

Erasure qubits reduce overhead in fault-tolerant quantum error correction (QEC) by converting dominant faults into detectable errors known as erasures. They have demonstrated notable improvements in thresholds and scaling in surface and Floquet code memories. In this work, we use erasure qubits on Bivariate Bicycle (BB) codes from the quantum low-density parity-check (QLDPC) regime. Owing to their sparse structure and favorable rate-distance trade-offs, BB codes are practical candidates for QEC. We introduce BiBiEQ, a novel framework that compiles a given BB code into an erasure-aware memory circuit $C_{E}$. This erasure circuit $C_{E}$ comprises erasure checks (ECs), resets, and erasures spread over a user-specified erasure check schedule (2EC, 4EC). BiBiEQ converts this erasure circuit $C_{E}$ into the stabilizer circuit $C$ for general-purpose decoding. BiBiEQ provides two engines for this conversion, BiBiEQ-Exact and BiBiEQ-Approx. BiBiEQ-Exact preserves the joint-erasure correlations and serves as our accuracy benchmark, while BiBiEQ-Approx uses an independence approximation to accelerate large sweeps and expose accuracy-throughput tradeoffs. Using BiBiEQ, we decode the stabilizer circuits to get a per-round logical error rate (LER) for the BB codes and quantify the effect of the EC schedules on the correctable operating region below the pseudo-threshold. The $4 E C$ schedule keeps the accuracy of both engines close to one another, making BiBiEQ-Approx a reliable proxy for BiBiEQ-Exact for faster sweeps. Below the pseudo-threshold, the code distance ($d$) hop from $d: 6 \rightarrow 10$ yields a drop in LER by $\approx 10-17 \times$ larger than $d: 10 \rightarrow 12$, showing that most gains are realized by $d=10$.

A. Al-Bayaty, Ali Al-shuwaili, A. AlZubaidi, M. Perkowski

Conventionally, the quantum oracular algorithms, such as Deutsch-Jozsa, Bernstein-Vazirani, and Grover, utilize Hadamard gates to create uniform superposition states for the input qubits of an oracle. However, Hadamard gates are non-native (non-supported) gates in all real quantum computers. For this reason, Hadamard gates are considered cost-expensive gates when realizing (transpiling) such algorithms into a real quantum computer. This paper introduces a new methodology for cost-effective transpilation of Grover's algorithm into real quantum computers, by replacing all Hadamard gates with \(\sqrt {\rm{X}} \) gates. In quantum computing, the Hadamard and \(\sqrt {\rm{X}} \) gates create uniform superposition states of a qubit on the X-axis and Y-axis of the Bloch sphere, respectively. Hence, we utilize a different axis of the Bloch sphere to construct a cost-effective realization for the final transpiled circuit of Grover's algorithm. Theoretically, the final transpiled circuit of Grover's algorithm using \(\sqrt {\rm{X}} \) gates has been minimized to approximately 64% than the circuit of Grover's algorithm using Hadamard gates. Experimentally, because of the \(\sqrt {\rm{X}} \) is an IBM native gate, it has been proven that the final transpiled circuit of Grover's algorithm using \(\sqrt {\rm{X}} \) gates always has a lower quantum cost of 6% than the circuit of Grover's algorithm using Hadamard gates, due to the limited layout (architecture) connectivity of the physical neighboring qubits for IBM quantum computers.

Jan Ostergaard

We propose a notion of compression-aware entanglement efficiency that accounts for the communication cost associated with distributing entangled qubits. Our analysis focuses on a class of pure bipartite quantum states constructed as amplitude-weighted superpositions of Dicke states. By optimizing the amplitude distribution and the choice of Hamming weights, we demonstrate that it is possible to maximize the ratio of entanglement entropy to the number of qubits that must be transmitted. This approach offers a principled way to quantify and enhance the efficiency of entanglement distribution in quantum communication protocols. The proposed symmetric states are highly compressible, and for $n=2,3,4$, and 6 qubits, they achieve the optimal entanglement efficiency rates.

Bhavesh Prajapati, Riddhi Prajapati

An interesting approach to using noisy intermediate-scale quantum (NISQ) devices is hybrid classical-quantum machine learning (QML). In these methods, classical processors handle optimisation and large-scale computation, while quantum hardware is devoted to tasks like feature mapping, nonlinear transformations, or kernel evaluation. Practical near-term demonstrations are made possible by this division of labour, which also mitigates existing hardware restrictions. In specialised fields like molecular modelling, materials discovery, and small-sample learning issues, recent advancements in variational quantum circuits, hybrid neural networks, and quantum kernel techniques have produced promising outcomes. However, scalability and wider applicability are still hampered by enduring issues including noise, barren plateaus, and the expense of repeated measurements. Long-term developments will require fault tolerance, logical qubits, and established software infrastructures, whereas near-term success depends on noise-aware algorithm design, repeatable experimental benchmarks, and enhanced error-mitigation strategies. When taken as a whole, these advancements show a viable path to achieving quantum advantage in machine learning. Quanta 2026; 15: 1–12.