VC pouring into Quantum, Quantum Bio for Drug Discovery, and the Threat to Bitcoin

Tech startup Silicon Quantum Computing has raised $50.4 million in a Series A funding round, with contributions from CBA, the University of New South Wales (UNSW), Telstra, and the Australian Government. Founded in 2017 by Michelle Simmons, a Scientia Professor at UNSW, the company aims to develop commercial quantum computers. The company aims to commercialize its product within three to five years. Quantum technology is gaining traction in Australia, with other startups like Quantum Brilliance raising over $25 million in funding for their miniaturized quantum computers that use synthetic diamonds to operate at room temperature.

Japanese company Next Logistics Japan is using quantum computing technology to enhance the efficiency of mixed-cargo trucking. In a recent demonstration involving 42 companies, a quantum computer calculated the most efficient way to load various package shapes and contents onto trucks, a task that used to take hours but is now completed in just 40 seconds. This technology allows companies to share trucks and jointly transport over 1,000 types of products. The warehouse involved in the demonstration also utilizes self-driving forklifts with cameras and sensors to unload goods from trucks. The initiative aims to automate the freight trucking process, leading to increased cargo transportation with fewer drivers and trucks.

Quantinuum achieved a groundbreaking milestone in quantum chemistry by simulating the hydrogen molecule using a partially fault-tolerant algorithm on its quantum processor with logical qubits.

One of the challenges in quantum computing has been dealing with noise in quantum processors, which hampers traditional algorithms from scaling up for larger problems. However, Quantinuum's scientists used the quantum-phase-estimation technique (QPE) with logical qubits, showcasing a path towards scalability. QPE is a fundamental technique in quantum computing with applications in computational chemistry, offering the potential to accelerate molecular simulations and the discovery of new materials. Ilyas Khan, Chief Product Officer at Quantinuum, emphasized that this is the first time QPE has been implemented on logical qubits.

Recent advancements in quantum computing pose a potential threat to Bitcoin's security and existence. While estimates vary, even a few thousand qubits could potentially compromise Bitcoin's cryptographic hashes, leading to issues in blockchain integrity and mining. Quantum algorithms like **Grover's and **Shor's pose risks to cryptographic hashing and encryption, respectively. To protect against quantum attacks, Bitcoin may need to undergo a fork and implement quantum-resistant encryption. Experts' estimates for the number of qubits required to crack Bitcoin's security range from a few thousand to billions, with some predicting this level may be achieved within the next decade. In the meantime, researchers are working on post-quantum cryptography to mitigate these risks.

Gero, an AI-driven biotech focused on aging and longevity, has demonstrated the use of quantum computing for drug design and generative chemistry. The research, published in Scientific Reports, used a hybrid quantum-classical machine-learning model to generate novel chemical structures for potential drugs. The model combined a generative chemistry algorithm (DVAE) with a D-Wave quantum annealer. The team produced 2,331 novel chemical structures with drug-like properties and high novelty. Quantum computing's potential in drug discovery lies in efficiently exploring the vast structural space of drug-like molecules. As quantum hardware evolves, fully quantum generative models could further enhance drug design capabilities. The research offers significant promise for future healthcare advancements.

Quantum computing has the potential to revolutionize energy grid optimization, particularly in addressing sustainability and climate-related challenges. D-Wave's Murray Thom, VP of quantum business innovation, highlights the shift from a hub and spoke model to a distributed model with various renewable energy capacities in energy companies' plans. Quantum computing is well-suited for complex optimization tasks related to energy flow in such distributed scenarios. One of the major challenges in the energy industry is reliability, especially considering the diverse ages of electrical grid infrastructure components. Quantum computing's ability to simulate and predict "cascade of failures" scenarios makes it a valuable tool for decision-making in complex networks.