Quantum Computing

Core quantum computing developments, breakthroughs, and innovations

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The Worst Day for Silver in 46 Years Serves as a Warning for the Stock Market's 2 Hottest Trends: AI and Quantum Computing
quantum-computing

The Worst Day for Silver in 46 Years Serves as a Warning for the Stock Market's 2 Hottest Trends: AI and Quantum Computing

Over the last three years, the bulls have ruled the roost on Wall Street. All of the stock market's major indexes have climbed to several record highs, with game-changing innovations and hot trends leading the way. This includes the rise of artificial intelligence (AI), the advent of quantum computing, and the precious metals bonanza that saw silver and gold catapult to all-time highs. But when things seem too good to be true on Wall Street, they often are. It's a lesson silver investors learned firsthand last week, and it's a plain-as-day warning for AI and quantum computing stock investors going forward. Image source: Getty Images. Silver's worst day since March 1980 offers a stark warning to investors Although AI and quantum computing have dominated investing headlines, it's silver that's delivered the outsize returns over the last year. Before the wildest trading session precious metal investors have witnessed in 46 years on Jan. 30, silver futures were approaching a nearly 300% return over the trailing year. There are certainly fundamental catalysts that have fueled this rally. For example, precious metals can be driven higher by supply and demand. Silver is a critical component used in solar panels and batteries for electric vehicles. As renewable energy usage proliferates, demand for this lustrous metal is expected to climb. Both gold and silver were also clear beneficiaries of a rapid rise in U.S. money supply during and after the COVID-19 pandemic. Gold and, to a lesser extent, silver are viewed as stores of value amid a seemingly ever-growing money supply. Whereas the physical allocation of gold and silver on planet Earth is finite (i.e., we can't create any additional gold or silver), U.S. dollars are continually printed by the U.S. Treasury Department, based on the Federal Reserve's prevailing monetary policy. But these fundamental factors took a back seat to something far more dangerous over the last two months: the fear of missing out, or FOMO. Watchin

Apple CEO Tim Cook Just Gave Great News to Micron Investors
quantum-computing

Apple CEO Tim Cook Just Gave Great News to Micron Investors

By Keith Speights – Feb 3, 2026 at 2:55AM ESTKey PointsMemory chip supply was a major topic during Apple's fiscal 2026 Q1 earnings call.Apple's memory constraints translate to great news for Micron.Micron is a dirt-cheap AI stock that could keep its momentum going this year.No one mentioned Micron in Apple's latest earnings call. They didn't have to.When Apple (AAPL +4.12%) provides quarterly updates, it only makes sense that everyone focuses on how the latest news impacts Apple itself. Sometimes, though, the information provided by Apple's management team has ramifications for other companies. That was the case with the iPhone maker's fiscal year 2026 first-quarter earnings call last week. Apple CEO Tim Cook just gave great news for Micron Technology (MU +5.42%) investors. Image source: Micron Technology. Apple's memorable earnings call If I counted correctly, the word "memory" was mentioned 16 times during Apple's Q1 earnings call. And the references weren't about fond recollections of Apple's history. Both analysts and Apple's management discussed the ongoing supply demand imbalance in memory chips extensively. Cook said on the call that the robust demand for iPhones put Apple in "a supply chase mode" for memory. He added, "We are currently constrained, and at this point, it is difficult to predict when supply and demand will balance." How have the supply constraints impacted Apple financially? Not very much, at least so far. Cook said that "memory had a minimal impact" on gross margins in Q1. However, he expects a greater impact in the second quarter. Anyone familiar with the law of supply and demand knows that prices rise when supply is limited and demand is strong. Unsurprisingly, Cook acknowledged that Apple expects memory prices to increase "significantly" beyond Q2. Why Cook's cautionary words are great news for Micron To be clear, Cook never mentioned Micron during Apple's Q1 earnings call. Neither did anyone else. However, Cook's cautionary words about su

Is IonQ the Top Quantum Computing Stock to Buy Right Now?
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Is IonQ the Top Quantum Computing Stock to Buy Right Now?

By Keithen Drury – Feb 3, 2026 at 12:22AM ESTKey PointsIonQ is leading the way in quantum computing accuracy. There is still plenty of time for other companies developing their own versions of the technology to catch up. NYSE: IONQIonQMarket Cap$14BToday's Changeangle-down(-3.55%) $1.42Current Price$38.56Price as of February 2, 2026 at 4:00 PM ETIonQ currently has the most accurate quantum computing solution based on one key metric.The level of hype around quantum computing seems to cycle up and down. For example, excitement about it surged to peaks during December 2024 and October 2025, but lately, it has significantly declined. While the nascent technology and the companies attempting to bring it to market are still on some investors' radar, they have fallen entirely off of most people's. That could make now a prime opportunity to buy some quantum computing stocks at lower prices, and that's exactly what I did. After missing some of IonQ's (IONQ 3.55%) major rises in 2025, I recently bought its shares, which are now down by more than 50% from their high. I think it's a great option in the quantum computing space, but is it the top buy overall? Image source: Getty Images. IonQ's fidelity advantage  The quantum computing field is filled with competitors, large and small. IonQ is among the smaller pure-play upstarts with relatively few resources that are looking to become major players. But many giant tech companies with nearly unlimited resources to devote to R&D are also competing in the quantum computing realm, as this new technology could bolster their already formidable capabilities. There are viable competitors across the size spectrum, but the reality is, we're a long way off from knowing which ones will eventually be the winners. ExpandNYSE: IONQIonQToday's Change(-3.55%) $-1.42Current Price$38.56Key Data PointsMarket Cap$14BDay's Range$37.72 - $40.7352wk Range$17.88 - $84.64Volume22MAvg Vol21MGross Margin-747.41% Most companies in the quan

Spooky action nearby: Entangling logical qubits without physical operations
quantum-computing

Spooky action nearby: Entangling logical qubits without physical operations

My top 10 ghosts (solo acts and ensembles). If Bruce Willis being a ghost in The Sixth Sense is a spoiler, that’s on you — the movie has been out for 26 years. Einstein and I have both been spooked by entanglement. Einstein’s experience was more profound: in a 1947 letter to Born, he famously dubbed it spukhafte Fernwirkung (or spooky action at a distance). Mine, more pedestrian. It came when I first learned the cost of entangling logical qubits on today’s hardware. Logical entanglement is not easy I recently listened to a talk where the speaker declared that “logical entanglement is easy,” and I have to disagree. You could argue that it looks easy when compared to logical small-angle gates, in much the same way I would look small standing next to Shaquille O’Neal. But that doesn’t mean 6’5” and 240 pounds is small. To see why it’s not easy, it helps to look at how logical entangling gates are actually implemented. A logical qubit is not a single physical object. It’s an error-resistant qubit built out of several noisy, error-prone physical qubits. A quantum error-correcting (QEC) code with parameters [[n,k,d]][\![n,k,d]\!] uses nn physical qubits to encode kk logical qubits in a way that can detect up to d−1d-1 physical errors and correct up to ⌊(d−1)/2⌋\lfloor (d-1)/2 \rfloor of them. This redundancy is what makes fault-tolerant quantum computing possible. It’s also what makes logical operations expensive. On platforms like neutral-atom arrays and trapped ions, the standard approach is a transversal CNOT: you apply two-qubit gates pairwise across the code blocks (qubit ii in block A interacts with qubit ii in block B). That requires nn physical two-qubit gates to entangle the kk logical qubits of one code block with the kk logical qubits of another. To make this less abstract, here’s a QuEra animation showing a transversal CNOT implemented in a neutral-atom array. This animation is showing real experimental data, not a schematic idealization. The idea is simple. T

QSPE: Enumerating Skeletal Quantum Programs for Quantum Library Testing
quantum-computing

QSPE: Enumerating Skeletal Quantum Programs for Quantum Library Testing

--> Quantum Physics arXiv:2602.00024 (quant-ph) [Submitted on 17 Jan 2026] Title:QSPE: Enumerating Skeletal Quantum Programs for Quantum Library Testing Authors:Jiaming Ye, Fuyuan Zhang, Shangzhou Xia, Xiaoyu Guo, Xiongfei Wu, Jianjun Zhao, Yinxing Xue View a PDF of the paper titled QSPE: Enumerating Skeletal Quantum Programs for Quantum Library Testing, by Jiaming Ye and 6 other authors View PDF HTML (experimental) Abstract:The rapid advancement of quantum computing has led to the development of various quantum libraries, empowering compilation, simulation, and hardware backend interfaces. However, ensuring the correctness of these libraries remains a fundamental challenge due to the lack of mature testing methodologies. The state-of-the-art tools often rely on domain-specific configurations and expert knowledge, which limits their accessibility and scalability in practice. Furthermore, although these tools demonstrate strong performance, they adopt measurement-based for output validation in testing, which makes them produce false positive reports. To alleviate these limitations, we propose QSPE, a practical approach that follows the differential testing principle and extends the existing approach, SPE, for quantum libraries. QSPE is fully automated, requiring no pre-set configurations or domain expertise, and can effectively generate a large set of diverse program variants that comprehensively explore the quantum compilation space. To mitigate the possible false positive reports, we propose statevector-based validation as an alternative to measurement-based validation. In our experiments, the QSPE approach demonstrates remarkable effectiveness in generating 22,770 program variants across multiple quantum computing platforms. By avoiding $\alpha$-equivalence at the quantum and classical program wise, QSPE can reduce redundant generation and save more than 90\% of execution cost. Finally, the statevector-based validation method assists QSPE to reduce false alarms an

Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning
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Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning

--> Quantum Physics arXiv:2602.00048 (quant-ph) [Submitted on 19 Jan 2026] Title:Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning Authors:Fan Fan, Yilei Shi, Mihai Datcu, Bertrand Le Saux, Luigi Iapichino, Francesca Bovolo, Silvia Liberata Ullo, Xiao Xiang Zhu View a PDF of the paper titled Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning, by Fan Fan and 7 other authors View PDF HTML (experimental) Abstract:Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and hi

Integrity from Algebraic Manipulation Detection in Trusted-Repeater QKD Networks
quantum-computing

Integrity from Algebraic Manipulation Detection in Trusted-Repeater QKD Networks

--> Quantum Physics arXiv:2602.00069 (quant-ph) [Submitted on 20 Jan 2026] Title:Integrity from Algebraic Manipulation Detection in Trusted-Repeater QKD Networks Authors:Ailsa Robertson, Christian Schaffner, Sebastian R. Verschoor View a PDF of the paper titled Integrity from Algebraic Manipulation Detection in Trusted-Repeater QKD Networks, by Ailsa Robertson and 1 other authors View PDF HTML (experimental) Abstract:Quantum Key Distribution (QKD) allows secure communication without relying on computational assumptions, but can currently only be deployed over relatively short distances due to hardware constraints. To extend QKD over long distances, networks of trusted repeater nodes can be used, wherein QKD is executed between neighbouring nodes and messages between non-neighbouring nodes are forwarded using a relay protocol. Although these networks are being deployed worldwide, no protocol exists which provides provable guarantees of integrity against manipulation from both external adversaries and corrupted intermediates. In this work, we present the first protocol that provably provides both confidentiality and integrity. Our protocol combines an existing cryptographic technique, Algebraic Manipulation Detection (AMD) codes, with multi-path relaying over trusted repeater networks. This protocol achieves Information Theoretic Security (ITS) against the detection of manipulation, which we prove formally through a sequence of games. Subjects: Quantum Physics (quant-ph); Cryptography and Security (cs.CR) Cite as: arXiv:2602.00069 [quant-ph]   (or arXiv:2602.00069v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2602.00069 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ailsa Robertson [view email] [v1] Tue, 20 Jan 2026 15:44:56 UTC (42 KB) Full-text links: Access Paper: View a PDF of the paper titled Integrity from Algebraic Manipulation Detection in Trusted-Repeater QKD Networks, by Ailsa Rob

A generating-function approach to the interference of squeezed states with partial distinguishability
quantum-computing

A generating-function approach to the interference of squeezed states with partial distinguishability

--> Quantum Physics arXiv:2602.00071 (quant-ph) [Submitted on 20 Jan 2026] Title:A generating-function approach to the interference of squeezed states with partial distinguishability Authors:Matheus Eiji Ohno Bezerra, Valery Shchesnovich View a PDF of the paper titled A generating-function approach to the interference of squeezed states with partial distinguishability, by Matheus Eiji Ohno Bezerra and 1 other authors View PDF Abstract:Photon distinguishability is a fundamental property manifested in multiphoton interference and one of the main sources of noise in any photonic quantum information processing. In this work, rather than relying on first-quantization methods, we build on a generating-function framework based on the phase-space formalism to characterize the effects of partial distinguishability on the interference of single-mode squeezed states. Our approach goes beyond commonly used models that represent distinguishability via additional noninterfering modes and captures genuine multiphoton interference effects induced by the overlap of the internal state of the photons. This description provides a clear physical account of how distinguishability gives rise to effective noise in Gaussian boson sampling protocols while enabling a systematic investigation of phase effects arising from the overlap of the internal states. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.00071 [quant-ph]   (or arXiv:2602.00071v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2602.00071 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Matheus Eiji Ohno Bezerra [view email] [v1] Tue, 20 Jan 2026 17:50:15 UTC (358 KB) Full-text links: Access Paper: View a PDF of the paper titled A generating-function approach to the interference of squeezed states with partial distinguishability, by Matheus Eiji Ohno Bezerra and 1 other authorsView PDFTeX Source view license Current browse context: quant-ph < prev &nbsp

Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery
quantum-computing

Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery

--> Quantum Physics arXiv:2602.00156 (quant-ph) [Submitted on 29 Jan 2026] Title:Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery Authors:Jaya Vasavi Pamidimukkala, Himanshu Sahu, Ashwini Kannan, Janani Ananthanarayanan, Kalyan Dasgupta, Sanjib Senapati View a PDF of the paper titled Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery, by Jaya Vasavi Pamidimukkala and 4 other authors View PDF HTML (experimental) Abstract:Genome sequencing is essential to decode genetic information, identify organisms, understand diseases and advance personalized medicine. A critical step in any genome sequencing technique is genome assembly. However, de novo genome assembly, which involves constructing an entire genome sequence from scratch without a reference genome, presents significant challenges due to its high computational complexity, affecting both time and accuracy. In this study, we propose a hybrid approach utilizing a quantum computing-based optimization algorithm integrated with classical pre-processing to expedite the genome assembly process. Specifically, we present a method to solve the Hamiltonian and Eulerian paths within the genome assembly graph using gate-based quantum computing through a Higher-Order Binary Optimization (HOBO) formulation with the Variational Quantum Eigensolver algorithm (VQE), in addition to a novel bitstring recovery mechanism to improve optimizer traversal of the solution space. A comparative analysis with classical optimization techniques was performed to assess the effectiveness of our quantum-based approach in genome assembly. The results indicate that, as quantum hardware continues to evolve and noise levels diminish, our formulation holds a significant potential to accelerate genome sequencing by offering faster and more accurate solutions to the complex challenges in genomic research. Subjects: Quantum Physics (quant-ph); Genomics (q-bi