Top Mathematics discussions
NishMath - #quantum
|
@thequantuminsider.com
//
References:
medium.com
, mrtecht.medium.com
,
The rise of quantum computing is creating a new era of strategic competition, with nations and organizations racing to prepare for the potential disruption to modern encryption. Quantum computers, leveraging qubits that can exist in multiple states simultaneously, have the potential to break current encryption standards, revolutionize fields like medicine and finance, and reshape global power dynamics. Governments and businesses are acutely aware of this threat, with the U.S. scrambling to implement quantum-resistant cryptography and China investing heavily in quantum networks. This competition extends to technology controls, with the U.S. restricting China's access to quantum technology, mirroring actions taken with advanced semiconductors.
The urgency stems from the fact that a cryptanalytically relevant quantum computer capable of breaking common public key schemes like RSA or ECC is anticipated by 2030. To address this, the National Institute of Standards and Technology (NIST) has standardized quantum-secure algorithms and set a 2030 deadline for their implementation, alongside the depreciation of current cryptographic methods. Companies like Utimaco are launching post-quantum cryptography (PQC) application packages such as Quantum Protect for its u.trust General Purpose HSM Se-Series, enabling secure migration ahead of the quantum threat. This package supports NIST-standardized PQC algorithms like ML-KEM and ML-DSA, as well as stateful hash-based signatures LMS and XMSS. Efforts are also underway to secure blockchain technology against quantum attacks. Blockchains rely on cryptography techniques like public-key cryptography and hashing to keep transactions secure, however, quantum computers could potentially weaken these protections. Post-quantum cryptography focuses on developing encryption methods resistant to quantum attacks. Key approaches include Lattice-Based Cryptography, which uses complex mathematical structures that quantum computers would struggle to solve. The transition to a quantum-resistant future presents challenges, including the need for crypto-agility and the development of secure migration strategies. Recommended read:
References :
@phys.org
//
References:
phys.org
, The Quantum Insider
,
A research team of statisticians from Cornell University has developed a novel data representation method inspired by quantum mechanics. This innovative approach aims to address the growing challenges posed by big, noisy data, which often overwhelms traditional data analysis techniques. The method works by simplifying large data sets and effectively filtering out noise, leading to more efficient data handling.
This breakthrough leverages the mathematical structures of quantum mechanics to better understand the underlying structure of complex data. According to Martin Wells, a professor of Statistical Sciences at Cornell, physicists have developed quantum mechanics-based tools that offer concise mathematical representations of complex data and the team is borrowing from these tools to understand the structure of data. Unlike conventional intrinsic dimension estimation techniques, which can be easily disrupted by noise and complexity, this quantum-inspired approach is more robust and accurate. The potential applications of this method are vast, particularly in data-rich fields like healthcare and epigenetics, where traditional methods have struggled. While quantum computing promises unprecedented speed, some experts debate its true potential, with efforts focused on "dequantizing" quantum algorithms to achieve comparable speeds using classical counterparts. This new data representation method offers a practical and accessible way to harness the principles of quantum mechanics on classical computers, potentially unlocking new insights from previously intractable data sets. Recommended read:
References :
Greg Bock@The Quantum Insider
//
References:
The Quantum Insider
Quantum computing has taken a significant leap forward with Phasecraft's development of a novel quantum simulation method called THRIFT (Trotter Heuristic Resource Improved Formulas for Time-dynamics). This breakthrough, detailed in a recent *Nature Communications* publication, drastically improves simulation efficiency and lowers computational costs, bringing real-world quantum applications closer to reality. THRIFT optimizes quantum simulations by prioritizing interactions with different energy scales within quantum systems, streamlining their implementation into smaller, more manageable steps.
This approach allows for larger and longer simulations to be executed without the need for increased quantum circuit size, thereby reducing computational resources and costs. In benchmarking tests using the 1D transverse-field Ising model, a widely used benchmark in quantum physics, THRIFT achieved a tenfold improvement in both simulation estimates and circuit complexities, enabling simulations that are ten times larger and run ten times longer compared to traditional methods. This development holds immense promise for advancements in materials science and drug discovery. Separately, mathematicians have achieved a breakthrough in understanding and modeling melting ice and other similar phenomena through a new proof that resolves long-standing issues related to singularities. A powerful mathematical technique used to model melting ice and other phenomena had been hampered by “nightmare scenarios.” A new proof has removed that obstacle. This new proof addresses concerns about "nightmare scenarios" that previously hindered the analysis of these processes, ensuring that singularities do not impede the continued evolution of the surface being modeled. The resolution, described in Quanta Magazine, allows mathematicians to more effectively assess the surface's evolution even after a singularity appears. Finally, researchers at Cornell University have introduced a novel data representation method inspired by quantum mechanics that tackles the challenge of handling big, noisy data sets. This quantum statistical approach simplifies large data sets and filters out noise, allowing for more efficient analysis than traditional methods. By borrowing mathematical structures from quantum mechanics, this technique enables a more concise representation of complex data, potentially revolutionizing innovation in data-rich fields such as healthcare and epigenetics where traditional methods have proven insufficient. Recommended read:
References :
@sciencedaily.com
//
Recent advancements in quantum computing research have yielded promising results. Researchers at the University of the Witwatersrand in Johannesburg, along with collaborators from Huzhou University in China, have discovered a method to shield quantum information from environmental disruptions, potentially leading to more reliable quantum technologies. This breakthrough involves manipulating quantum wave functions to preserve quantum information, which could enhance medical imaging, improve AI diagnostics, and strengthen data security by providing ultra-secure communication.
UK startup Phasecraft has announced a new algorithm, THRIFT, that improves the ability of quantum computers to model new materials and chemicals by a factor of 10. By optimizing quantum simulation, THRIFT enables scientists to model new materials and chemicals faster and more accurately, even on today’s slower machines. Furthermore, Oxford researchers have demonstrated a 25-nanosecond controlled-Z gate with 99.8% fidelity, combining high speed and accuracy in a simplified superconducting circuit. This achievement advances fault-tolerant quantum computing by improving raw gate performance without relying heavily on error correction or added hardware. Recommended read:
References :
Matt Swayne@The Quantum Insider
//
D-Wave Quantum Inc. has made a splash by claiming its Advantage2 annealing quantum computer achieved quantum supremacy in complex materials simulations, publishing their study in the journal Science. The company states that its system can perform simulations in minutes that would take the Frontier supercomputer nearly a million years and consume more than the world’s annual electricity consumption. According to D-Wave CEO Alan Baratz, this achievement validates quantum annealing's practical advantage and represents a major milestone in quantum computational supremacy and materials discovery.
However, D-Wave's claim has faced criticism, with researchers suggesting that classical algorithms can rival or even exceed quantum methods in these simulations. Some researchers say that they performed similar calculations on a normal laptop in just two hours. Concerns have been raised about the real-world applicability and practical benefits of D-Wave's quantum supremacy claims in computational tasks. Despite the criticisms, D-Wave is standing by the claims from the study. Recommended read:
References :
Siôn Geschwindt@The Next Web
//
Dutch quantum hardware company QuantWare B.V. has secured €20 million in a Series A funding round to scale its quantum processors for next-generation computing. The round was co-led by Invest-NL Deep Tech Fund and Innovation Quarter, with participation from EIC Fund and existing investors. QuantWare develops VIO, a technology that allows its customers to build larger single-chip quantum processing units, which are less prone to interference.
QuantWare's VIO technology aims to solve scaling bottlenecks that limit the size of QPUs today, allowing users to scale any qubit design and unlocking the fastest path towards quantum computers with more than 1 million qubits in a single processor. The funding will be used to further develop VIO, expand chip fabrication facilities, and roll out QuantWare’s Contralto-A QPU, designed for quantum error correction. QuantWare's CEO, Matthijs Rijlaarsdam, stated that their mission is to make VIO the scaling standard and power the first million-qubit quantum computers of the hyperscalers of tomorrow. Recommended read:
References :
Alyssa Hughes (2ADAPTIVE LLC dba 2A Consulting)@Microsoft Research
//
Microsoft has announced two major advancements in both quantum computing and artificial intelligence. The company unveiled Majorana 1, a new chip containing topological qubits, representing a key milestone in its pursuit of stable, scalable quantum computers. This approach uses topological qubits, which are less susceptible to environmental noise, aiming to overcome the long-standing instability issues that have challenged the development of reliable quantum processors. The company says it is on track to build a new kind of quantum computer based on topological qubits.
Microsoft is also introducing Muse, a generative AI model designed for gameplay ideation. Described as a first-of-its-kind World and Human Action Model (WHAM), Muse can generate game visuals and controller actions. The company says it is on track to build a new kind of quantum computer based on topological qubits. Microsoft’s team is developing research insights to support creative uses of generative AI models. Recommended read:
References :
@investorplace.com
//
References:
| InvestorPlace
The debate surrounding the timeline for quantum computing is intensifying, with differing views emerging from tech leaders. SAP CEO Christian Klein has recently challenged skepticism, asserting that quantum computing is much closer than the 15 to 30 years predicted by some in the industry. Klein argues that quantum technologies can soon tackle complex problems like supply-chain management, improving simulation speeds and offering new efficiencies in logistics. These claims contrast with other leaders' predictions that the technology's significant impact is still years away, contributing to volatility in the market, with quantum computing stocks experiencing a turbulent ride.
Despite the uncertainty surrounding the timeline, there's a consensus that quantum computing represents a transformative leap in technology. The power of quantum physics offers the potential to create computers that are infinitely faster and more powerful. The debate centers on when these breakthroughs will materialize and how they will impact various sectors. There is evidence of advancements in quantum computing, such as the development of error correction techniques, and the study of how matrices can be applied to quantum computing. However, challenges related to energy requirements, error correction, and maintaining quantum coherence remain ongoing concerns for some critics. Recommended read:
References :
|
Blogs
|